论文标题
使用顺序gans建模低资源语言的图形分析
Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs
论文作者
论文摘要
已经证明，生成的对抗网络（GAN）有助于在很难获得大量实际数据的情况下创建人工数据。在计算语言学领域，这个问题尤其重要，在计算语言学领域，研究人员通常负责模拟低资源语言的复杂形态和语法过程。本文将讨论试图仅使用100个示例字符串对语言进行建模和重现语言的图形动物的GAN的实现和测试。这些人工且符合图形符合的字符串旨在帮助建模低资源语言的形态变化。
Generative Adversarial Networks (GANs) have been shown to aid in the creation of artificial data in situations where large amounts of real data are difficult to come by. This issue is especially salient in the computational linguistics space, where researchers are often tasked with modeling the complex morphologic and grammatical processes of low-resource languages. This paper will discuss the implementation and testing of a GAN that attempts to model and reproduce the graphotactics of a language using only 100 example strings. These artificial, yet graphotactically compliant, strings are meant to aid in modeling the morphological inflection of low-resource languages.
